
"""simple docstring"""





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()



def 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_							):
		__SCREAMING_SNAKE_CASE				=					TapasConfig.from_json_file(__snake_case							)
		# set absolute/relative position embeddings parameter
		__SCREAMING_SNAKE_CASE				=					reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				__SCREAMING_SNAKE_CASE				=					TapasForQuestionAnswering(config=__snake_case							)
		elif task == "WTQ":
				# run_task_main.py hparams
				__SCREAMING_SNAKE_CASE				=					4
				__SCREAMING_SNAKE_CASE				=					True
				# hparam_utils.py hparams
				__SCREAMING_SNAKE_CASE				=					0.664_694
				__SCREAMING_SNAKE_CASE				=					0.207_951
				__SCREAMING_SNAKE_CASE				=					0.121_194
				__SCREAMING_SNAKE_CASE				=					True
				__SCREAMING_SNAKE_CASE				=					True
				__SCREAMING_SNAKE_CASE				=					False
				__SCREAMING_SNAKE_CASE				=					0.0_352_513

				__SCREAMING_SNAKE_CASE				=					TapasForQuestionAnswering(config=__snake_case							)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				__SCREAMING_SNAKE_CASE				=					4
				__SCREAMING_SNAKE_CASE				=					False
				# hparam_utils.py hparams
				__SCREAMING_SNAKE_CASE				=					36.4_519
				__SCREAMING_SNAKE_CASE				=					0.903_421
				__SCREAMING_SNAKE_CASE				=					222.088
				__SCREAMING_SNAKE_CASE				=					True
				__SCREAMING_SNAKE_CASE				=					True
				__SCREAMING_SNAKE_CASE				=					True
				__SCREAMING_SNAKE_CASE				=					0.763_141

				__SCREAMING_SNAKE_CASE				=					TapasForQuestionAnswering(config=__snake_case							)
		elif task == "TABFACT":
				__SCREAMING_SNAKE_CASE				=					TapasForSequenceClassification(config=__snake_case							)
		elif task == "MLM":
				__SCREAMING_SNAKE_CASE				=					TapasForMaskedLM(config=__snake_case							)
		elif task == "INTERMEDIATE_PRETRAINING":
				__SCREAMING_SNAKE_CASE				=					TapasModel(config=__snake_case							)
		else:
				raise ValueError(f"Task {task} not supported."							)

		print(f"Building PyTorch model from configuration: {config}"							)
		# Load weights from tf checkpoint
		load_tf_weights_in_tapas(__snake_case    ,							__snake_case    ,							__snake_case							)

		# Save pytorch-model (weights and configuration)
		print(f"Save PyTorch model to {pytorch_dump_path}"							)
		model.save_pretrained(__snake_case							)

		# Save tokenizer files
		print(f"Save tokenizer files to {pytorch_dump_path}"							)
		__SCREAMING_SNAKE_CASE				=					TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""    ,							model_max_length=512							)
		tokenizer.save_pretrained(__snake_case							)

		print("""Used relative position embeddings:"""    ,							model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
							__magic_name__			=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
							)
							parser.add_argument(
							    "--reset_position_index_per_cell",
							    default=False,
							    action="store_true",
							    help="Whether to use relative position embeddings or not. Defaults to True.",
							)
							parser.add_argument(
							    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
							)
							parser.add_argument(
							    "--tapas_config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained TAPAS model. \n"
							        "This specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							__magic_name__			=     parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.task,
							    args.reset_position_index_per_cell,
							    args.tf_checkpoint_path,
							    args.tapas_config_file,
							    args.pytorch_dump_path,
							)












"""simple docstring"""
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case : Dict     		=		logging.get_logger(__name__)



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""input_values""", """padding_mask"""]







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	int = 1				,    UpperCamelCase_ :	int = 2_4_0_0_0				,    UpperCamelCase_ :	float = 0.0				,    UpperCamelCase_ :	float = None				,    UpperCamelCase_ :	float = None				,    **UpperCamelCase_ :	str				,    ):





           '''simple docstring'''

           super().__init__(feature_size=UpperCamelCase_				,    sampling_rate=UpperCamelCase_				,    padding_value=UpperCamelCase_				,    **UpperCamelCase_ )
           __magic_name__      				=    chunk_length_s
           __magic_name__      				=    overlap







    @property
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           if self.chunk_length_s is None:
                  return None
           else:
                  return int(self.chunk_length_s * self.sampling_rate )







    @property
    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           if self.chunk_length_s is None or self.overlap is None:
                  return None
           else:
                  return max(1				,    int((1.0 - self.overlap) * self.chunk_length ) )







    def __call__(		self :	List[Any]				,    UpperCamelCase_ :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,    UpperCamelCase_ :	Optional[Union[bool, str, PaddingStrategy]] = None				,    UpperCamelCase_ :	Optional[bool] = False				,    UpperCamelCase_ :	Optional[int] = None				,    UpperCamelCase_ :	Optional[Union[str, TensorType]] = None				,    UpperCamelCase_ :	Optional[int] = None				,    ):





           '''simple docstring'''

           if sampling_rate is not None:
                  if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                             f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                             f""" {self.sampling_rate} and not {sampling_rate}.""" )
           else:
                  logger.warning(
                      'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                      'Failing to do so can result in silent errors that might be hard to debug.' )

           if padding and truncation:
                  raise ValueError('Both padding and truncation were set. Make sure you only set one.' )
           elif padding is None:
                  # by default let's pad the inputs
                  __magic_name__      				=    True

           __magic_name__      				=    bool(
               isinstance(UpperCamelCase_				,    (list, tuple) ) and (isinstance(raw_audio[0]				,    (np.ndarray, tuple, list) )) )

           if is_batched:
                  __magic_name__      				=    [np.asarray(UpperCamelCase_				,    dtype=np.floataa ).T for audio in raw_audio]
           elif not is_batched and not isinstance(UpperCamelCase_				,    np.ndarray ):
                  __magic_name__      				=    np.asarray(UpperCamelCase_				,    dtype=np.floataa )
           elif isinstance(UpperCamelCase_				,    np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
                  __magic_name__      				=    raw_audio.astype(np.floataa )

           # always return batch
           if not is_batched:
                  __magic_name__      				=    [np.asarray(UpperCamelCase_ ).T]

           # verify inputs are valid
           for idx, example in enumerate(UpperCamelCase_ ):
                  if example.ndim > 2:
                         raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}""" )
                  if self.feature_size == 1 and example.ndim != 1:
                         raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels""" )
                  if self.feature_size == 2 and example.shape[-1] != 2:
                         raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels""" )

           __magic_name__      				=    None
           __magic_name__      				=    BatchFeature({'input_values': raw_audio} )
           if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
                  if truncation:
                         __magic_name__      				=    min(array.shape[0] for array in raw_audio )
                         __magic_name__      				=    int(np.floor(max_length / self.chunk_stride ) )
                         __magic_name__      				=    (nb_step - 1) * self.chunk_stride + self.chunk_length
                  elif padding:
                         __magic_name__      				=    max(array.shape[0] for array in raw_audio )
                         __magic_name__      				=    int(np.ceil(max_length / self.chunk_stride ) )
                         __magic_name__      				=    (nb_step - 1) * self.chunk_stride + self.chunk_length
                         __magic_name__      				=    'max_length'
                  else:
                         __magic_name__      				=    input_values

        # normal padding on batch
           if padded_inputs is None:
                  __magic_name__      				=    self.pad(
                      UpperCamelCase_				,    max_length=UpperCamelCase_				,    truncation=UpperCamelCase_				,    padding=UpperCamelCase_				,    return_attention_mask=UpperCamelCase_				,    )
                  if padding:
                         __magic_name__      				=    padded_inputs.pop('attention_mask' )

           __magic_name__      				=    []
           for example in padded_inputs.pop('input_values' ):
                  if self.feature_size == 1:
                         __magic_name__      				=    example[..., None]
                  input_values.append(example.T )

           __magic_name__      				=    input_values
           if return_tensors is not None:
                  __magic_name__      				=    padded_inputs.convert_to_tensors(UpperCamelCase_ )

           return padded_inputs